Abstract
The paper treats a multi-object tracking approach based on a multi-layer particle filter which is able to deal with the class of unclustered spatially extended measurements. The particle filter uses a so called adaptive layer distribution spanned over the tracking space, which determines the particles’ extents. Since the particle extents are used for the calculation of the particle weights, the multi-modal posterior distribution representing the dynamic objects is approximated with locally different resolutions. Moreover, the layer distribution is used to detect new appearing objects through a reinitialization step. In order to extract an object list out of the particle density, an Expectation Maximization (EM) clustering is used. The basic algorithm is extended with an estimation of the currently necessary number of clusters. The developed tracking approach is evaluated by means of image measurement data out of a roundabout scene. The proposed approach enhances tracking quality and robustness compared to a conventional approach.
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Notes
- 1.
The usage of randomly selected particles instead of the particles with the lowest weights reduces sample impoverishment [2].
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This work was partially supported by the German Ministry of Education and Research BMBF as part of the project AHeAD.
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Buyer, J., Vollert, M., Kocsis, M., Sußmann, N., Zöllner, R. (2018). Multi-object Tracking Based on a Multi-layer Particle Filter for Unclustered Spatially Extended Measurements. In: Lee, S., Ko, H., Oh, S. (eds) Multisensor Fusion and Integration in the Wake of Big Data, Deep Learning and Cyber Physical System. MFI 2017. Lecture Notes in Electrical Engineering, vol 501. Springer, Cham. https://doi.org/10.1007/978-3-319-90509-9_13
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